Rapid improvements in computational power and coincident increases in the amount of data on individuals and firms that is publicly available or can be purchased at a nominal cost have created new challenges for protecting the privacy of survey responses and administrative data. By combining such data with information from external sources, it is increasingly possible to breach the anonymity of individuals and businesses and their characteristics. Such breaches can violate the strong privacy protections statistical agencies and other data providers are required or pledge to uphold. Statistical agencies, such as the U.S. Census Bureau, have responded by adopting new disclosure avoidance systems, including model-based synthetic data methods and methods based on the criterion known as differential privacy.

All of these methods, as well as those that have been used in the past, face an inevitable trade-off between data accuracy and protecting the privacy of individuals and firms.  There has been substantial research on the extent to which these approaches protect privacy and on the accuracy of the data that results from their application.  Less attention has been paid to issues such as (a) the implications of using these data for conducting applied research and for estimation and inference, (b) quantifying the actual disclosure risks of alternative disclosure avoidance methods and how individuals, firms and society value these risks; and (c) how to best align the data needs for applied research with the obligations to protect privacy.

To provide new insights on the use of privacy-protected data in empirical research, the National Bureau of Economic Research (NBER), with the support of the Alfred P. Sloan Foundation, is launching a new research project.  The project, headed by Ruobin Gong (Rutgers University), V. Joseph Hotz (Duke University and NBER), and Ian Schmutte (University of Georgia), will promote interactions among researchers from computer science, data science, economics, statistics, and other fields who will present their research at two conferences, one to be held in Cambridge on May 4-5, 2023, and the other in Washington, DC, in late 2023 or early 2024.

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